The Novel Hemoglobin-based Oxygen Carrier HRC 101 Improves Survival in Murine Sickle Cell Disease
Bibliographic record
Abstract
BACKGROUND: Erythrocyte transfusion decreases morbidity in sickle cell disease, but is not without risk. Use of a hemoglobin-based oxygen carrier could offer the benefits of erythrocyte transfusion while reducing related complications. The authors tested the hypothesis that the novel hemoglobin-based oxygen carrier, HRC 101, would improve survival during exposure to acute hypoxia in a murine model of sickle cell disease, the transgenic mouse expressing hemoglobin SAD (alpha2beta2). METHODS: Wild-type (n = 30) and transgenic SAD (n = 36) mice received 0.02 ml/g HRC 101 (hemoglobin concentration, 10 g/dl) or an equal volume of 5% albumin. Thirty percent or 6% oxygen was administered to spontaneously breathing mice during halothane anesthesia (inspired concentration, 0.5%). The time to cessation of cardiac electrical activity was recorded. Survival was compared using Kaplan-Meier analysis. RESULTS: Control mice survived the 60-min study period, whether breathing 30% or 6% oxygen. In contrast, all SAD mice given albumin and 6% oxygen died, with a median survival time of 9.0 min (interquartile range, 6.9-11.6 min; P < 0.0001). HRC 101 significantly increased survival in SAD mice breathing 6% oxygen. Of 12 SAD mice given HRC 101 and 6% oxygen, 4 survived the entire study period and 8 died, with a median survival time of 48 min (19-60 min; P < 0.0001 vs. albumin). CONCLUSION: HRC 101 significantly decreased sickle-related mortality during exposure to acute hypoxic stress in transgenic mice expressing hemoglobin SAD. HRC 101 warrants further evaluation as a therapeutic modality in sickle cell disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".